1 citations · 1 across the 5 of their papers we have counts for
8 papers
Lean Graph Search for Formal Theorem Proving
Yihan Xu, Jia-Chen Gu, Junyi Zhang +7
Retrieving relevant lemmas is crucial for formal theorem proving, enabling provers to build machine-checkable proofs from established mathematical results. Recent retrieval systems…
Continual Graph Memory for Mathematical Research Agents
Junyi Zhang, Jinxi Yu, Eric Hanchen Jiang +14
Using frontier agent harnesses to tackle mathematical research problems has emerged as an effective means of advancing mathematics. However, solving frontier problems in mathematic…
Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging
Ruichen Zheng, Yihe Wang, Fabrice Y Harel-Canada +4
LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set may not guarantee valid measurem…
From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier
Eric Jiang, Xiao Liang, Yikai Zhang +19
Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for we…
SLAM: Structural Linguistic Activation Marking for Language Models
Fabrice Harel-Canada, Amit Sahai
LLM watermarks must be detectable without compromising text quality, yet most existing schemes bias the next-token distribution and pay for detection with measurable quality loss.…
TaoBench: Do Automated Theorem Prover LLMs Generalize Beyond MathLib?
Alexander K Taylor, Junyi Zhang, Ethan Ji +10
Automated theorem proving (ATP) benchmarks largely consist of problems formalized in MathLib, so current ATP training and evaluation are heavily biased toward MathLib's definitiona…
Sandcastles in the Storm: Revisiting the (Im)possibility of Strong Watermarking
Fabrice Y Harel-Canada, Boran Erol, Connor Choi +4
Watermarking AI-generated text is critical for combating misuse. Yet recent theoretical work argues that any watermark can be erased via random walk attacks that perturb text while…
Measuring Psychological Depth in Language Models
Fabrice Harel-Canada, Hanyu Zhou, Sreya Muppalla +4
Evaluations of creative stories generated by large language models (LLMs) often focus on objective properties of the text, such as its style, coherence, and diversity. While these…